Just days before the election, my final forecast went against the wisdom of professional forecasters and pollsters alike and projected a rail-thin electoral margin for Joe Biden. While the election results surprised many people on the night of November 3, my model’s point prediction anticipated an even closer race in the electoral college–273 electoral votes for Biden compared to his actual 306–but a wider spread in the popular vote–52.8% compared to his actual 51.9%.
The statistical aphorism that “all models are wrong, but some are useful” served as my guiding philosophy in constructing this model. As I discussed in my final prediction, I did not expect this model to perfectly forecast all outcomes in the election. Rather, this forecast aimed to provide a range of state-level probabilities and outcomes. Then, I used the most probable state-level outcomes to produce point predictions for the Electoral College and national popular vote. While these numbers could be interpreted as my “final prediction”, I would have been incredibly shocked if the exact outcomes matched perfectly due to the wide amount of variability in my simulations.
The actual Electoral College outcome, with each candidate winning the states that they won on Election Night, occurred in 53 of my simulations. To put that into perspective, my point prediction occurred in 5080 of my simulations, which equates to 0.051%. Forecasters cannot predict the election outcome with absolute certainty, but models provide a range of possible scenarios. This model successfully anticipated a close Electoral Race with a large popular vote margin, and the actual outcome occurred more than a handful of times in my simulations.
All in all, I’m quite happy with how this model paralleled with the election outcomes. It only misclassified the winner of GA, NV, and AZ, which were three of the final states called. Even though the model predicted that a Donald Trump victory was more likely in these states, the forecast predicted a close race in those states and gave either candidate a fair shot of winning–Joe Biden won GA, NV, and AZ in 19.2%, 43.9%, and 20.5% of simulations, respectively. In my nationwide election simulations, the exact election outcome occurred in 57 out of 100,000 (0.057%) simulations. Interpreting these probabilities with a frequentist1 approach, those probabilities could have very well been correct and we just happened to observe one of the 53 elections where each candidate won this exact cocktail of states.
With a correlation of 0.9608654 between the actual and the predicted two-party popular vote for each state, there is an incredibly strong correlation between the actual and predicted state-level two-party vote shares. With that said, there are a few patterns in the inaccuracies:
On average, Joe Biden underperformed his predicted vote share by -0.28 percentage points relative to the forecast. As visible in the below scatterplot, Joe Biden’s actual vote share fell short of the model’s predictions in the Democrat-leaning states and exceeded the predicted vote share in the Republican-leaning states.
Despite overpredicting Joe Biden’s vote share in most states, the model underestimated Joe Biden’s performance in the only three misclassified states. Essentially, the model overestimated Joe Biden’s vote share in general but underestimated it in the states with incorrect point predictions.
The below maps illustrate the areas with the greatest error. Notice that safe blue and red states such as New York and Louisiana have relatively large errors, while battleground states such as Texas and Ohio have extremely slim errors. For a closer look at the data, the included table contains all of the actual and predicted two-party vote shares for Joe Biden, ordered by the magnitude of the error:
| State | Actual Democratic Two-Party Vote Share | Predicted Democratic Two-Party Vote Share | Error |
|---|---|---|---|
| NY | 57.24493 | 69.61151 | -12.3665780 |
| RI | 60.50962 | 69.52752 | -9.0179003 |
| HI | 65.03633 | 72.32903 | -7.2927031 |
| LA | 40.53556 | 33.93166 | 6.6039010 |
| SC | 44.07339 | 37.81854 | 6.2548501 |
| DE | 59.62674 | 65.87647 | -6.2497293 |
| AR | 35.78478 | 29.62813 | 6.1566468 |
| AK | 44.71671 | 40.02248 | 4.6942271 |
| NJ | 58.00345 | 62.50579 | -4.5023339 |
| CA | 65.06225 | 69.50769 | -4.4454369 |
| CT | 60.17662 | 64.60562 | -4.4289997 |
| WA | 59.95101 | 64.18870 | -4.2376851 |
| ND | 32.78259 | 36.80377 | -4.0211801 |
| OR | 58.33341 | 62.25845 | -3.9250389 |
| MA | 66.86435 | 70.77922 | -3.9148649 |
| NE | 40.24716 | 44.02699 | -3.7798311 |
| WV | 30.20202 | 33.80620 | -3.6041767 |
| KS | 42.25143 | 38.65813 | 3.5932950 |
| MN | 53.63371 | 50.05711 | 3.5765990 |
| MS | 40.59077 | 37.20396 | 3.3868054 |
| GA | 50.14253 | 47.01611 | 3.1264220 |
| ME | 55.14282 | 52.09217 | 3.0506449 |
| SD | 36.56522 | 39.42158 | -2.8563608 |
| AZ | 50.15683 | 47.34474 | 2.8120875 |
| MT | 41.60337 | 38.79975 | 2.8036197 |
| MO | 42.06354 | 39.50587 | 2.5576671 |
| AL | 37.03289 | 34.62090 | 2.4119937 |
| KY | 36.79758 | 34.47128 | 2.3263011 |
| IN | 41.79335 | 39.56968 | 2.2236715 |
| VA | 55.15706 | 57.35345 | -2.1963895 |
| NV | 51.22312 | 49.36777 | 1.8553463 |
| TN | 38.11647 | 36.32844 | 1.7880338 |
| NM | 55.51576 | 53.81917 | 1.6965921 |
| CO | 56.93974 | 58.48604 | -1.5463005 |
| UT | 39.30687 | 37.82175 | 1.4851174 |
| NC | 49.31589 | 48.10486 | 1.2110359 |
| IL | 58.45714 | 59.62805 | -1.1709124 |
| IA | 45.81652 | 46.91440 | -1.0978824 |
| VT | 68.29919 | 67.26910 | 1.0300846 |
| MI | 51.36015 | 50.53204 | 0.8281138 |
| NH | 53.74888 | 53.06014 | 0.6887330 |
| WY | 27.51957 | 26.85758 | 0.6619846 |
| FL | 48.30525 | 48.95294 | -0.6476975 |
| MD | 66.62085 | 67.16643 | -0.5455827 |
| OK | 33.05996 | 32.60532 | 0.4546372 |
| TX | 47.12886 | 46.69227 | 0.4365935 |
| ID | 34.12328 | 33.75413 | 0.3691501 |
| OH | 45.85491 | 45.99202 | -0.1371140 |
| PA | 50.60314 | 50.68815 | -0.0850104 |
| WI | 50.31728 | 50.35326 | -0.0359886 |
Since this model was not unilaterally biased like most other forecast models, this model’s average error is considerably closer to zero than other popular forecasts, and the errors are more normally distributed around zero:
| Model | Mean Error | Root Mean Squared Error | Classification Accuracy | Missed States |
|---|---|---|---|---|
| Kayla Manning | -0.2804308 | 3.874984 | 94 | AZ, GA, NV |
| The Economist | -2.3310087 | 2.803927 | 96 | FL, NC |
| FiveThirtyEight | -2.4447961 | 3.019431 | 96 | FL, NC |
As with any forecast model that incorporated polls, this forecast would have benefited from improved polling accuracy. Unfortunately, I do not control the polling methodology, so I must improve my model in other ways. I applied an aggressive weighting scheme based on FiveThirtyEight’s pollster grades in an attempt to control for polling bias. In spite of these efforts, the model still produced extreme predictions in either direction, with a more favorable outcome for Biden in the liberal states and predicted a more favorable outcome for Trump in conservative states. The diverging direction of the inaccuracies leads me to consider other potential causes for the inaccuracies and potential improvements for future iterations of this model.
This model neglected to pick up on the magnitude of changing views in states such as Arizona and Georgia, both of which voted for Trump in 2016 yet voted for Biden in 2020.2 To account for this in 2024 and beyond, I could include a variable that captures shifting partisanship within a state between elections. In this model, I attempted to use demographic changes as a proxy for this, but a more direct variable might work better. I plan on incorporating a “difference in Democratic vote share” variable in future iterations of this model, which looks at the difference in the share of that state’s two-party popular vote in the two previous elections. For 2024, I would each state’s Democratic vote share in 2016 from the Democratic vote share in 2020. Negative numbers indicate Republican trends and positive numbers would indicate Democratic shifts, with larger absolute values indicating a shift of greater magnitude.
To assess this hypothesis, I could reconstruct the model, following the same procedures as outlined in my final prediction. I would use the same data from 1992-2016 and include this variable that captures the state-level changes in voting patterns between elections. Once I have constructed this new model, I could assess its validity in several ways:
Finally, I would compare this model’s 2020 forecast to my previous model. If this model more accurately predicted the state-level outcomes, then I know that my 2024 should resemble this newer model. However, if my previous model performed better in the leave-one-out classification and on the 2020 data, then I would stick with my original, more parsimonious model for the future.
Aside from the lack of a variable to capture shifting partisan alignment within states, there are several other modifications I would make to the methodology behind this model in a future iteration. I touched on many of these in greater detail in my final prediction post, but here is a brief overview:
While my forecast did not perfectly predict the election outcomes, this model correctly projected a relatively close race in the Electoral College with a larger margin in the popular vote. Furthermore, the outcomes of November 3 all reasonably match the probabilities assigned by the model. Even in GA, NV, and AZ–the three misclassified states–the actual vote shares were not too far from the predictions, and both candidates had a fair probability of winning those states. Despite having predicted this election exceptionally well when many models did not, future iterations of this model must do a better job at accounting for partisan shifts within states (assuming this country survives to see another 4 years).
Unlike rolling dice, we cannot experience multiple occurrences of the same election to uncover the true probability of each event. Frequentist probability describes the relative frequency of an event in many trials; conducting many simulations in my model took a frequentist approach to uncover the probability of each outcome. However, we can never really know if any of the probabilities were correct because the 2020 election only happened once (thank goodness!). Trying to say whether or not a probabilistic forecast was correct is like rolling a “six” on a single die and concluding that your prior probabilities of 1/6 for rolling a 6 and 5/6 for rolling anything else were incorrect because you observed the less probable outcome on a single iteration.↩︎
However, any changes would have to keep in mind that FL, OH, WI, etc. were more conservative than most forecasts anticipated, and this model correctly anticipated the winner in these highly contentious battleground states.↩︎